JP5249696B2 - Vehicle driving support device - Google Patents

Description

  The present invention sets a risk for a traveling path and a three-dimensional object around the own vehicle detected by a navigation device, an in-vehicle camera, a millimeter wave radar, etc., and operates for warning, braking, steering assistance, etc. in order to travel on the optimum path. The present invention relates to a driving support device for a vehicle that performs support.

  In recent years, risk is set for obstacles, white lines, etc. present on the road, and the route that minimizes this risk is calculated as the route, and warning, braking, and steering assistance are performed based on the route information of this route A driving support device that controls the above has been developed.

For example, in Japanese Patent Application Laid-Open No. 2007-253745, a risk potential field in which roads on which a vehicle runs, road boundaries, obstacles existing on the roads are detected, risks are set for each of these, and these risks are added together. Discloses a technique for performing driving support control by setting an avoidance route so as to be as close as possible to a region having a low value of.
JP 2007-253745 A

  However, when the driving support control technique disclosed in Patent Document 1 described above is applied to an environment where there is an intersection, a risk based on a white line is not set at the intersection, and the minimum risk calculation is not executed correctly. was there. In addition, when the host vehicle is stopped at an intersection, the traveling locus of the host vehicle cannot be estimated, and a collision determination with an obstacle cannot be performed.

  The present invention has been made in view of the above circumstances, and can appropriately set the risk even at the intersection and perform the minimum risk calculation smoothly, continuously setting the avoidance route and determining the collision with the obstacle. An object of the present invention is to provide a vehicle driving support device that can be executed naturally and accurately.

The present invention includes a vehicle including a surrounding environment recognition unit that recognizes a surrounding environment of the host vehicle, a risk setting unit that sets a risk for the surrounding environment, and a control unit that supports driving based on the set risk. In the driving support apparatus, the vehicle has an intersection information acquisition unit that acquires intersection information ahead of the host vehicle, and a right / left turn detection unit that detects a right / left turn of the host vehicle. detects the right turn, when setting the risk corresponding to the right turn onto the intersection from one lane through the intersection when the right turn in the other lane, when detecting the above other lanes Is characterized by comprising an intersection risk setting means for setting the risk set in the other lane in preference to the risk set on the intersection.

  According to the vehicle driving support device of the present invention, the minimum risk can be calculated smoothly by appropriately setting the risk even at the intersection, and the avoidance route setting and the collision determination with the obstacle are continuously performed. It becomes possible to execute naturally and accurately.

Hereinafter, embodiments of the present invention will be described with reference to the drawings.
1 to 9 show an embodiment of the present invention, FIG. 1 is a schematic configuration diagram of a driving support device mounted on a vehicle, FIG. 2 is a flowchart of a driving support control program, and FIG. 3 is a flowchart continuing from FIG. FIG. 4 is a flowchart of a current risk function calculation routine for a white line, FIG. 5 is an explanatory diagram showing an example of a risk function set in front, FIG. 6 is an explanatory diagram of risk set at the time of turning left and right at an intersection, 7 is an explanatory diagram of the risk set at the intersection based on information from the navigation device, FIG. 8 is an explanatory diagram of the risk set at the intersection based on information different from FIG. 7 from the navigation device, and FIG. It is explanatory drawing which shows an example of the avoidance route and turning control amount which are produced | generated.

  In FIG. 1, reference numeral 1 denotes a vehicle such as an automobile (own vehicle), and a driving support device 2 is mounted on the vehicle 1. The driving support device 2 includes a stereo camera 3, a stereo image recognition device 4, a control unit 5, and the like as main parts.

  Further, the host vehicle 1 is provided with a vehicle speed sensor 11 for detecting the host vehicle speed V, a yaw rate sensor 12 for detecting the yaw rate (dψ / dt), and the host vehicle speed V is determined by the stereo image recognition device 4 and the control unit 5. And the yaw rate (dψ / dt) is input to the control unit 5. Further, an operation signal of a turn signal switch 13 as a right / left turn detection means operated by a driver is also input to the control unit 5.

  The stereo camera 3 is composed of a set of (left and right) CCD cameras using a solid-state imaging device such as a charge coupled device (CCD) as a stereo optical system. These left and right CCD cameras are respectively mounted at a certain interval in front of the ceiling in the vehicle interior, take a stereo image of an object outside the vehicle from different viewpoints, and input image data to the stereo image recognition device 4.

  The processing of the image from the stereo camera 3 in the stereo image recognition device 4 is performed as follows, for example. First, distance information is obtained from a pair of stereo image pairs captured in the traveling direction of the host vehicle 1 taken by the stereo camera 3 from the corresponding positional shift amount, and a distance image is generated. Based on this data, a well-known grouping process is performed and compared with frames (windows) such as three-dimensional road shape data, side wall data, and three-dimensional object data stored in advance. White line data along the road and white line (stop line: intersection information) data crossing the road), side walls data such as guardrails and curbs that exist along the road, and three-dimensional objects as two-wheeled vehicles and ordinary vehicles Categorized and extracted into other three-dimensional objects such as large vehicles, pedestrians, utility poles.

  Each of the recognized data is calculated by calculating the position in the coordinate system in which the own vehicle 1 is the origin, the front-rear direction of the own vehicle 1 is the X axis, and the width direction is the Y axis. In the vehicle data of a large vehicle, the length in the front-rear direction is preliminarily estimated as 3 m, 4.5 m, 10 m, etc., and the width direction uses the center position of the detected width. The currently existing center position is calculated with the coordinates of (xobstacle, yobstacle). In addition, when the longitudinal length of the vehicle can be obtained with high accuracy by inter-vehicle communication or the like, the above-described center position may be calculated using the length data.

  Further, in the three-dimensional object data, the relative speed with respect to the own vehicle 1 is calculated from the change in the X-axis direction and the change in the Y-axis direction of the distance from the own vehicle 1, and the vector V is taken into consideration for the relative speed and the vector V Thus, the X-axis direction speed and the Y-axis direction speed (vxobstacle, vyobstacle) of each three-dimensional object are calculated.

  Each information obtained in this way, that is, white line data, guard rails existing along the road, side walls such as curbs, and solid object data (type, distance from own vehicle 1, center position (xobstacle, yobstacle), speed) (Vxobstacle, vyobstacle, etc.) is input to the control unit 5. Thus, in this Embodiment, the stereo camera 3 and the stereo image recognition apparatus 4 are provided as a surrounding environment recognition means and an intersection information acquisition means.

  The control unit 5 includes the vehicle speed sensor 11 to the host vehicle speed V, the yaw rate sensor 12 to the yaw rate (dψ / dt), the stereo image recognition device 4 to the white line data, the side data of guardrails, curbs, and the like existing along the road. Each data of the object data (type, distance from own vehicle 1, center position (xobstacle, yobstacle), speed (vxobstacle, vyobstacle), etc.) is input. Then, according to the driving support control program described later, based on each input signal described above, the current risk level is set as a risk function Rline, Robstacle for each of the white line, guardrail, side wall, and three-dimensional object existing ahead. Then, the current total risk function R is set from these risk functions Rline and Robstacle. After that, the temporal change of the position of each target for which the total risk function R is set is predicted to predict the temporal change of the total risk function R. Based on the temporal change of the total risk function R, each time The local minimum point ymin (x, t) in the Y-axis direction at the own vehicle position is calculated, and the deviation between the lateral position of the own vehicle 1 and the local minimum point ymin (x, t) and the turn control amount u (t) at each time. Then, the objective function J for each time is created, and the turn control amount u (t) for each time that minimizes the objective function J is calculated as the turn control amount u (t) of the host vehicle 1. Then, a risk function R (t) for each route when the host vehicle 1 moves with a turn control amount u (t) for each time is set, and a final risk function R (t) for each route is set. The avoidance route R (t) f is selected, a control signal is output to the automatic steering control device 23 based on the turning control amount u (t) of the final avoidance route R (t) f, and the steering control is executed. In addition, a signal is output to the automatic brake control device 22 based on the value of the final avoidance route R (t) f to execute the brake control. When signals are output to the automatic brake control device 22 and the automatic steering control device 23, the signals are visually displayed on the display 21 to notify the driver. That is, the control unit 5 has a function as a risk setting means and a control means.

Next, the driving support control program executed by the driving support device 2 will be described with reference to the flowcharts of FIGS.
First, in step (hereinafter, abbreviated as “S”) 101, necessary parameters, specifically, white line data, side data of guardrails, curbs, etc. existing along the road, and three-dimensional object data (type, own vehicle 1 Distance, center position (xobstacle, yobstacle), velocity (vxobstacle, vyobstacle), etc.) are read.

Next, proceeding to S102, the current risk function Rline for the white line is calculated according to the program shown in FIG.
That is, in S201, it is determined whether or not the host vehicle speed V is equal to or lower than a preset vehicle speed Vc (for example, 20 km / h) for determining low speed travel, and the host vehicle speed V is greater than the vehicle speed Vc (V> Vc). ), It is determined that the straight line will be continued, and the process proceeds to S202, where the current risk function Rline for the white line (the guardrail and the side wall are handled in the same way as the white line) is calculated by the following equation (1). Then exit the routine.
Rline = Kline · (y−ylinec) ² (1)
Here, Kline is a preset gain, and ylinec is a white line center coordinate. That is, as shown in FIG. 5, the current risk function Rline for the white line is 2 centered on the center of the road recognized by the left and right white lines (guardrails and side walls are also treated as white lines). Is given by In the present embodiment, the risk function Rline is a quadratic function. However, the risk function Rline may be a function that leads to a larger risk value as it approaches the white line from the center of the road, for example, 4 It can also be a second or sixth order function. In the present embodiment, the guard rail and the side wall are also treated in the same way as the white line so as to give a risk function Rline of a quadratic function. However, in the case of the guardrail and the side wall, the function is different from the risk function Rline for the white line. It may be changed so that a larger risk value is derived than in the case of the white line. For example, when the risk function Rline for the left and right white lines is given by a quadratic function, the card rail and the side wall are changed to a quartic or sixth order function. Further, even for the same quadratic function, the value of the gain Kline may be changed to a large value. Furthermore, the risk function Rline for the white line is not limited to the example in which the center of the travel path is the central axis, but the central axis may be offset so that the risk values differ between the left and right white lines.

  As a result of the determination in S201, if the host vehicle speed V is equal to or lower than the vehicle speed Vc (V ≦ Vc), the process proceeds to S203, and it is determined whether there is a stop line. If the result of this determination is that there is no stop line, it is determined that there is no intersection ahead, the process proceeds to S202 described above, the current risk function Rline is calculated from the above equation (1), and the routine is exited.

  On the other hand, if it is determined in S203 that there is a stop line, it is determined that there is an intersection, the process proceeds to S204, and it is determined whether the turn signal switch 13 is activated.

  If the turn signal switch 13 is not actuated as a result of this determination, it is determined that the host vehicle 1 does not make a right or left turn, and the process proceeds to S202 described above, and the current risk function is expressed by the above equation (1). Rline is calculated and the routine is exited. In this case, the current risk function Rline set in a portion without a white line (intersection portion) is set by extending and interpolating the detected white line in the front-rear direction.

  As a result of the determination in S204, if the turn signal switch 13 is operated, the process proceeds to S205, and it is determined whether or not it is a right turn. In the case of a right turn, the process proceeds to S206, where it is determined whether a white line is detected ahead. If a white line is detected ahead, the current risk function Rline is calculated giving priority to the detected white line. Accordingly, the process proceeds to S202, and the current risk function Rline is calculated by the above-described equation (1) to exit the routine.

If no white line is detected ahead, the process proceeds to S207, the risk function Rline at the time of turning right at the intersection is calculated by the following equation (2), and the routine is exited.
Rline = Kturnr · ((x ² + (y + RR) ² ) ^1/2 + RR) ² (2)
Here, Kturnr is a constant, and RR is a turning radius when turning right, which is set in advance. That is, as shown in FIG. 6, in the case of a right turn, when the host vehicle position is the origin, the host vehicle traveling path when the turning radius RR is used is obtained by the following equation (3).
x ² + (y + RR) ² = RR ² (3)

Further, the current risk function Rline increases or decreases in the tangential direction with respect to the own vehicle traveling path. Since the turning radius at the right turn of the travel locus is fixed to RR this time, when a polar coordinate system with the turning center as the origin is set, the following equation (4) is obtained.
Rline = Kturnr · (R + RR) ² (4)
Here, R is a turning radius of the host vehicle 1. When the above equation (4) is converted into the xy coordinate system, R = (x ² + (y + RR) ² ) ^1/2 from the equation (3). ) Formula is obtained.

  On the other hand, if it is determined in S205 that it is not a right turn (that is, a left turn), the process proceeds to S208, in which it is determined whether a white line is detected in front, and if a white line is detected in front, the detected white line is detected. Is advanced to S202 to calculate the current risk function Rline, and the current risk function Rline is calculated according to the above equation (1) to exit the routine.

If no white line is detected ahead, the process proceeds to S209, and the risk function Rline when turning left at the intersection is calculated by the following equation (5), and the routine is exited.
Rline = Kturnl · ((x ² + (y−RL) ² ) ^1/2 −RL) ² (5)
Here, Kturnl is a constant, and RL is a turning radius set in advance when turning left. That is, as shown in FIG. 6, in the case of a left turn, when the vehicle position is the origin, the vehicle traveling path when the turning radius RL is used is obtained by the following equation (6).
^{x 2 + (y-RL)} 2 = RL 2 ... (6)

Further, the current risk function Rline increases or decreases in the tangential direction with respect to the own vehicle traveling path. Since the turning radius at the time of the left turn of the travel locus is fixed to RL this time, if a polar coordinate system with the turning center as the origin is set, the following equation (7) is obtained.
Rline = Kturnl · (R−RL) ² (7)
Here, R is a turning radius of the host vehicle 1. When the above expression (7) is converted into the xy coordinate system, R = (x ² + (y−RL) ² ) ^1/2 from the expression (6). Equation (5) is obtained.

  In this embodiment, RR> RL is set. This is because when turning right at the intersection, it is expected to turn over the left lane of the lane to turn.

  The coefficients Kturnr and Kturnl for determining the size of the current risk function set at the intersection may be smaller than the current risk function coefficient Kline set for the white line. This is because the current risk function set for the white line can be regarded as a more accurate value.

  In the present embodiment, the presence or absence of an intersection is determined by determining the stop line in S203. However, the presence or absence of an intersection may be determined based on map information from the navigation device. For example, if the map information from the navigation device represents one-sided, one-lane roads with a single row of nodes, and the intersection of these roads (intersection) is represented by one node, As shown in FIG. 7 (a), the risk function Rline at the time of turning left at the intersection is set from the previously set position before the node of the intersection according to the above equation (5). Further, at the time of a right turn, as shown in FIG. 7B, the risk function Rline at the time of the right turn of the intersection is set from the above-described formula (2) from a preset position before the node of the intersection. In addition, when the map information from the navigation device is data representing one lane on each side as a single row of nodes, when turning left, as shown in FIG. 8A, the road indicated by the node on the near side of the intersection is shown. As a reference, a risk function Rline at the time of turning left at an intersection is set from the position set in advance from this road by the above-described equation (5). When turning right, as shown in FIG. 8 (b), with reference to the road indicated by the node at the back of the intersection, the risk of turning right at the intersection according to the above equation (2) from the position preset from this road The function Rline is set.

As described above, after the current risk function Rline is set in S102, the process proceeds to S103 to target a three-dimensional object (two-dimensional vehicle, ordinary vehicle, large vehicle, pedestrian, other three-dimensional object such as a utility pole). The current risk function Robstacle is calculated by the following equation (8).
Robstacle = Kobstacle · exp (− ((xobstacle−x) ² / (2 · σxobstacle ² ))
− ((Yobstacle-y) ² / (2 · σyobstacle ² ))) (8)
Here, Kobstacle is a preset gain. Further, σxobstacle indicates the dispersion in the X-axis direction of the preset object, σyobstacle indicates the dispersion in the Y-axis direction of the preset object, and these dispersions σxobstacle and σyobstacle are, for example, a stereo camera The smaller the recognition accuracy by 3, the larger the setting may be. Also, the variances σxobstacle and σyobstacle should be set larger when the target type is a pedestrian or two-wheeled vehicle, and smaller when it is a three-dimensional object. Anyway. Furthermore, you may make it set according to the lap | wrap rate of the width direction of the own vehicle 1 and the target solid object. In FIG. 5, the three-dimensional object A <b> 1 and the three-dimensional object A <b> 2 are examples of the current risk function Robtacle for the three-dimensional object calculated by the above-described equation (8).

Next, it progresses to S104 and the present total risk function R is calculated by the following (9) Formula.
R = Rline + Robstacle (9)

Next, in S105, the position of the three-dimensional object (xobstacle (t), yobstacle (t)) after t seconds is estimated by the following equation (10).
(Xobstacle (t), yobstacle (t))
= (Xobstacle + vxobstacle.t, yobstacle + vyobstacle.t) (10)

  Next, the process proceeds to S106, and the position of the three-dimensional object (xobstacle (t), yobstacle (t)) estimated in S105 is substituted into x and y of the total risk function R calculated in S104. Then, the total risk function R (xobstacle (t), yobstacle (t)) after t seconds is set.

Subsequently, the process proceeds to S107, and the total risk function R (xobstacle (t), yobstacle (t)) after t seconds set in S106 is partially differentiated in the width direction (y direction), and the value is 0. From this point, the minimum point ymin (x, t) in the width direction (y direction) is calculated. That is,
∂R (xobstacle (t), yobstacle (t)) / ∂y = 0 (11)
The point that becomes is the minimum point.

Next, the process proceeds to S108, and the own vehicle position (X (t), Y (t)) after t seconds is estimated by the following equation (12).
(X (t), Y (t)) = (V · t, V · ∫sinψ (τ) dτ; integration range is 0 ≦ τ ≦ t)
(12)
Here, ψ (t) is the yaw angle of the host vehicle 1, and is calculated by the following equation (13).
ψ (t) = (dψ / dt) · t
+ (1/2) · ((d ² ψ / dt ² ) + (u (t) / Iz)) · t ² (13)
Here, Iz is the yaw moment of inertia. U (t) is the turning control amount as described above, and is the additional yaw moment.

  Next, the process proceeds to S109, where the own vehicle position estimated in S108 is substituted into the y-direction minimum point ymin (x, t) calculated in S107, and the minimum point ymin ( Calculate X (t), t).

  Next, the process proceeds to S110, and each objective function J is created by the deviation of the lateral position Y (t) of the own vehicle and the minimum point ymin (X (t), t) and the turning control amount u (t) for each time. For each objective function J, a turn control amount u (t) for each time that minimizes the objective function J is obtained.

  For example, as shown in FIG. 9, a range in which the host vehicle 1 moves from time 0 (current) to Δt is considered as a control target region, and this region is divided by dt, and 1dt, 2dt, 3dt,..., Mdt,. , (N−2) dt, (n−1) dt, and ndt (= Δt).

For example, objective functions J0 to 1dt of the following equation (14) are set between times 0 and 1 dt, and the turning control amount u (0) that minimizes the objective functions J0 to 1dt is determined by a well-known optimization calculation. Ask.
J0 ~ 1dt = Wy · (ymin (X (1dt), 1dt) −Y (1dt)) ² + Wu · u (0) ² (14)
Here, Wy and Wu are preset weight values.

Further, for example, objective functions J1dt to 2dt of the following equation (15) are set between times 1dt and 2dt, and the turning control amount u (1dt) that minimizes the objective functions J1dt to 2dt is known optimization. Obtain by calculation.
J1dt ~ 2dt = Wy · (ymin (X (2dt), 2dt) −Y (2dt)) ² + Wu · u (1dt) ² (15)

Further, for example, the objective function J2dt to 3dt of the following equation (16) is set between the times 2dt to 3dt, and the turning control amount u (2dt) that minimizes the objective function J2dt to 3dt is known optimization. Obtain by calculation.
J2dt ~ 3dt = Wy · (ymin (X (3dt), 3dt) −Y (3dt)) ² + Wu · u (2dt) ² (16)
Since there are two minimum points at time 3dt, two values are also obtained for the turning control amount u (2dt).

Hereinafter, the same objective function is set after time 3dt, the turning control amount is obtained, and, for example, the objective function J (n-1) of the following equation (17) between times (n-1) dt and ndt. dt ~ ndt is set, and the turning control amount u ((n-1) dt) that minimizes the objective function J (n-1) dt ~ ndt is obtained by a known optimization calculation.
J (n-1) dt ~ ndt = Wy. (Ymin (X (ndt), ndt) -Y (ndt)) ²
+ Wu · u ((n-1) dt) ² (17)

  Next, in S111, the risk function R (t) for each route when the host vehicle 1 moves with the turn control amount u (t) for each time is set by the following equation (18).

R (t) = Rline + Robstacle (18)
Here, Rline and Robstacle are the above-mentioned formulas (1), (2), (5), and (8), respectively. It is given by the value when moving at (t),
Rline = Kline · (Y (t) −ylinec) ² (19)
Rline = Kturnr · ((X (t) ² + (Y (t) + RR) ² ) ^1/2 + RR) ² (20)
Rline = Kturnl · ((X (t) ² + (Y (t) −RL) ² ) ^1/2 −RL) ² (21)
Robstacle = Kobstacle · exp (− ((xobstacle (t) −X (t)) ²
/ (2 · σxobstacle ² ))-((yobstacle (t) -Y (t)) ²
/ (2 · σyobstacle ² )))… (22)

  Next, the process proceeds to S112, and a final avoidance route is selected as R (t) f from the risk function R (t) for each route set in S111.

Specifically, the maximum value Rmax is obtained for each route set in S111. That is,
Rmax = max (R (t)) (0 ≦ t ≦ Δt) (23)
Then, the route with the smallest maximum value Rmax is selected as the final avoidance route R (t) f.

  A cumulative risk value Rsum (= ∫R (t) dt; integration range is 0 ≦ t ≦ Δt) is obtained for each route, and a route having the smallest value is defined as a final avoidance route R (t) f. You may make it select.

  In S112 described above, if there is only one route set in S111, that route is set as the final avoidance route R (t) f.

  For example, in the example shown in FIG. 9, the route 1 indicated by the solid line and the route 2 indicated by the broken line are set by the process of S111, and the route having a smaller maximum value Rmax from these routes 1 and 2 by the process of S112, or A route having a small risk accumulation value Rsum is selected as a final avoidance route R (t) f. The turning control amounts u (t) for the routes 1 and 2 are as shown in FIG. 9B.

  Then, the process proceeds to S113, where it is determined whether or not there is a region that is equal to or greater than the predetermined maximum allowable risk value Rlim (R (t) f ≧ Rlim) in the final avoidance route R (t) f, and R ( t) If there is no region where f ≧ Rlim, the routine proceeds to S116, where the steering control command is issued to the automatic steering control device 23 based on the turning control amount u (t) of the final avoidance route R (t) f. To exit the program.

  As a result of the determination in S113, if it is determined that there is a region where R (t) f ≧ Rlim, the process proceeds to S114, and the braking start point Xbrake, based on the earliest time when R (t) f ≧ Rlim is satisfied. The braking start time Tbrake is calculated.

When Tm is the earliest time when R (t) f ≧ Rlim, the braking start point Xbrake is calculated by the following equation (24).
Xbrake = X (Tm) −Bx (24)
Here, Bx is a braking distance based on the deceleration G set in advance, and is calculated by the following equation (25).
Bx = (V ² /(2.G))+Bx0 (25)
Here, Bx0 is a preset distance to the obstacle when the vehicle is stopped, and is a value of about 2 m, for example.

  The braking start time Tbrake is calculated by calculating backward from the above-described braking start point Xbrake.

  Next, in S115, a braking control command based on the braking start point Xbrake and the braking start time Tbrake is output to the automatic brake control device 22.

  In S116, a steering control command is output to the automatic steering control device 23 based on the turning control amount u (t) of the final avoidance route R (t) f, and the program is exited.

  As described above, according to the embodiment of the present invention, the current total risk function R is set for each of the white line, the guardrail, the side wall, and the three-dimensional object existing in front, and the time of the position of each target is determined. A change is predicted to predict a temporal change of the total risk function R, and based on the temporal change of the total risk function R, a local minimum point ymin (x, t ) Is calculated. Then, an objective function J for each time is created, and the turn control amount u (t) for each time that minimizes the objective function J is calculated as the turn control amount u (t) of the host vehicle 1. The risk function R (t) for each route when the vehicle 1 moves with the turning control amount u (t) for each time is set, and the final avoidance route R is determined from the risk function R (t) for each route. (t) f is selected, steering control is executed based on the turning control amount u (t) of the final avoidance route R (t) f, and the final avoidance route R (t) f is set to the value. Based on this, brake control is executed. For this reason, it is possible to realize the collision avoidance control in consideration of not only the immediate danger but also the risk of coming ahead.

  In addition, according to the embodiment of the present invention, since the risk corresponding to the right / left turn of the host vehicle 1 is set even at the intersection where no white line exists, the minimum risk calculation is smoothly performed on most routes. Therefore, it is possible to execute avoidance route setting and collision determination with an obstacle continuously and naturally. At this time, the risk to be set at the intersection can be executed by a simple calculation in order to preliminarily estimate the turning radius necessary to turn right and left at the intersection and set the risk corresponding to the turning radius.

  In the present embodiment, an example in which brake control and steering control can be performed based on the final avoidance route R (t) f has been described, but either one may be performed. .

  Further, driving support may be performed by alarm control without performing driving support by brake control and steering control. In this case, similarly to the setting of the braking avoidance point Xbrake, the point that becomes the predetermined risk value earliest on the finally selected avoidance route R (t) f is obtained, and the point that is a predetermined distance before this point Is set as the alarm start point. And when the own vehicle 1 arrives at a warning start point, a warning is output.

  The brake control described in this embodiment is merely an example, and may be used in combination with other well-known brake control, for example, closing of the throttle opening or downshifting in an automatic transmission.

  Furthermore, in this embodiment, the surrounding environment is recognized based on the captured image from the stereo camera 3, but it may also be detected by a monocular camera, millimeter wave radar, or the like.

  In the present embodiment, the current total risk function R is set for a white line or a three-dimensional object in front of the host vehicle 1 and the temporal change thereof is predicted. However, the present invention is not limited to this. The setting of the total risk function R and its temporal change may be predicted for the three-dimensional object on the side or rear side of the vehicle 1.

  Furthermore, in the present embodiment, a configuration has been described in which an avoidance route is generated when the host vehicle 1 is moving forward. You may make it produce | generate.

Schematic configuration diagram of a driving support device mounted on a vehicle Flow chart of driving support control program Flowchart continuing from FIG. Flow chart of current risk function calculation routine for white line Explanatory drawing which shows an example of the risk function set ahead Explanatory diagram of risk set when turning left or right at the intersection Explanatory diagram of risk set at intersection based on information from navigation device Explanatory drawing of the risk set at the intersection based on information different from FIG. 7 from the navigation device Explanatory drawing which shows an example of the avoidance route and turning control amount which are produced | generated

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Own vehicle 2 Driving support device 3 Stereo camera (Ambient environment recognition means, intersection information acquisition means)
4 Stereo image recognition device (neighboring environment recognition means, intersection information acquisition means)
5 Control unit (risk setting means, control means)
11 Vehicle speed sensor 12 Yaw rate sensor 13 Turn signal switch (right / left turn detection means)
21 Display 22 Automatic brake control device 23 Automatic steering control device

Claims (3)

Driving support apparatus for a vehicle, comprising: surrounding environment recognition means for recognizing the surrounding environment of the host vehicle; risk setting means for setting a risk for the surrounding environment; and control means for driving support based on the set risk In
Intersection information acquisition means for acquiring intersection information ahead of the host vehicle, and right / left turn detection means for detecting right / left turn of the host vehicle,
The risk setting means detects a right / left turn at the intersection of the host vehicle, and sets a risk corresponding to the right / left turn on the intersection, and then makes a right / left turn from one lane to the other lane through the intersection. If the other lane is detected, there is provided an intersection risk setting means for setting the risk set in the other lane in preference to the risk set on the intersection. Driving assistance device.   2. The vehicle driving support device according to claim 1, wherein the intersection risk setting means estimates a turning radius required to turn left and right at the intersection, and sets a risk corresponding to the turning radius. The vehicle driving support device according to claim 1 or 2, wherein the intersection information acquisition means acquires at least one of image information from an in- vehicle camera and information from a navigation device.

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